Intelligent Sensor System for Early Illness Alerts in Senior Housing

用于老年住宅早期疾病警报的智能传感器系统

• 批准号: 8478491
• 负责人: MARILYN J RANTZ
• 金额: $ 62.99万
• 依托单位: UNIVERSITY OF MISSOURI-COLUMBIA
• 依托单位国家: 美国
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-05-16 至 2016-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8478491
• 关键词: Activities of Daily Living; Acute; Affect; Age; Agitation; Algorithms; American; Beds; Blood Glucose; Breathing; Caregivers; Chronic; Chronic Disease; Clinical effectiveness; Complex; Data; Detection; Devices; Disease Management; Early Diagnosis; Early Intervention; Early treatment; Elderly; Environment; Equipment; Gait; Hand; Health; Health Care Costs; Health Personnel; Health Status; Healthcare; Home Nursing Care; Home environment; Hospitalization; Housing; Intervention; Intervention Studies; Left; Length of Stay; Life; Measures; Methods; Monitor; Morbidity - disease rate; Motion; Nursing Homes; Participant; Patient Self-Report; Pattern; Physiologic pulse; Pilot Projects; Population; Prevention; Prospective Studies; Pulse Oximetry; Recovery; Recruitment Activity; Sample Size; Sampling; Sleep; Speed; System; Time; United States; Weight; Work; active control; base; cost; cost effectiveness; functional decline; functional improvement; grasp; improved; information display; interest; mortality; multiple chronic conditions; prospective; public health relevance; sensor; telehealth; web based interface

DESCRIPTION: Chronic disease management is the biggest health care problem facing the United States today. In 2005, nearly 1 in 2 Americans (133 million) had at least one chronic condition, and 21% of the population had multiple chronic conditions. These numbers will steadily increase over the next 30 years. Chronic diseases especially affect older adults in whom exacerbations result in dramatic changes and decline in health status, hospitalization, complex treatment interventions, and high cost. Early illness recognition and early treatment is not only a key to improving health status with rapid recovery after an exacerbation of a chronic illness or acute illness, but also a key to reducing morbidity and mortality in older adults and controlling costs of health care. We propose to build on our recently completed pilot intervention study (NINR R21, Rantz, PI). In that study, we developed alerts for our intelligent sensor system (not telehealth that measures traditional vital signs, weight, pulse oximetry, blood sugar) and used it prospectively to measure functional ability in older adults and actually detected changes in chronic diseases or acute illnesses on average 10 days to 2 weeks before usual assessment methods or self-reports of illness. Inexpensive sensors are embedded in the environment, so subjects do not "have to use" or "wear" any devices. The R21 was to: 1) develop alerts based on the sensor data to notify health care providers of early signs of illness or functional decline so they could further evaluate and intervene with early treatment; 2) further develop and refine a web-based interface to display the sensor data to health care providers; and 3) determine the sample size for an intervention study that would measure the clinical effectiveness and cost-effectiveness of using the sensor system with alerts in elder housing. Now, we propose to conduct a prospective intervention study to measure the clinical effectiveness and cost effectiveness of using sensor data to detect early signs of illness or functional decline in older adults compared to usual health assessment. A larger sample of older adults will be recruited; they live in a different independent housing than where the pilot study was conducted. While preparing the staff in the different housing setting to work with alerts from our intelligent sensor system, we will adjust, if necessary, the algorithms for automated alerts or the web-based interface for health care providers. Following the prospective study, we will develop and refine ways of providing sensor information to older adults and informal caregivers to help them directly better manage changes in health status. Our intelligent sensor system enables early detection of illness or functional decline, the key to successful less invasive, time consuming, and expensive interventions. Helping older adults remain healthier, active, and control their chronic illnesses with early detection of changes in health status and early intervention by health care providers, can result in millions of people remaining independent as they age, avoiding or reducing debilitating and costly hospital stays, and for many, avoiding or delaying nursing home care. This application will be of interest to both NINR and NIA.

慢性病管理是当今美国面临的最大卫生保健问题。在2005年，近1/2的美国人（1.33亿）至少有一种慢性疾病，21%的人口有多种慢性疾病。这些数字将在未来30年稳步增长。慢性病尤其影响老年人，他们的病情加重会导致健康状况的急剧变化和下降，住院治疗，复杂的治疗干预和高昂的费用。早期疾病识别和早期治疗不仅是改善健康状况，在慢性病或急性病恶化后迅速康复的关键，也是降低老年人发病率和死亡率以及控制保健费用的关键。 我们建议以我们最近完成的试点干预研究（NINR R21，Rantz，PI）为基础。在这项研究中，我们为我们的智能传感器系统（不是测量传统生命体征，体重，脉搏血氧饱和度，血糖的远程医疗）开发了警报，并前瞻性地使用它来测量老年人的功能能力，并在通常的评估方法或自我报告疾病之前平均10天到2周实际检测到慢性疾病或急性疾病的变化。廉价的传感器嵌入环境中，因此受试者不必“使用”或“佩戴”任何设备。R21是：1）基于传感器数据开发警报，以通知医疗保健提供者疾病或功能衰退的早期迹象，以便他们可以进一步评估并干预早期治疗; 2）进一步 发展及改善网上界面，向医护人员展示感应器数据;及3）厘定干预研究的样本数目，以评估在长者住屋使用感应器系统的临床成效及成本效益。 现在，我们建议进行一项前瞻性干预研究，以衡量与通常的健康评估相比，使用传感器数据检测老年人疾病或功能下降的早期迹象的临床有效性和成本效益。将招募更多的老年人样本;他们住在与进行试点研究不同的独立住房中。当不同住房环境中的工作人员准备好处理来自我们智能传感器系统的警报时，我们将在必要时调整自动警报的算法， 为医疗保健提供者提供的基于网络的界面。在前瞻性研究之后，我们将开发和完善向老年人和非正式护理人员提供传感器信息的方法，以帮助他们更好地管理健康状况的变化。我们的智能传感器系统可以早期检测疾病或功能衰退，这是成功的关键， 消耗性的，昂贵的干预措施。帮助老年人保持健康，活跃，并通过早期发现健康状况的变化和医疗保健提供者的早期干预来控制他们的慢性疾病，可以使数百万人随着年龄的增长而保持独立，避免或减少使人虚弱和昂贵的住院时间，对许多人来说，避免或推迟疗养院护理。NINR和NIA都将对该应用程序感兴趣。